Growth factor for therapeutic angiogenesis in ischemic heart disease: A meta-analysis of randomized controlled trials
Bibliographic record
Abstract
Aim: This study was designed to systematically evaluate the effects of growth factor (GF) for therapeutic angiogenesis on ischemic heart disease (IHD) by pooling the results of randomized controlled trials (RCTs). Methods and Results: PubMed, EMBASE, and CENTRAL databases were searched from inception to October 2022. RCTs, investigating the effects of GF therapy on IHD, were included. The risk bias of included study was assessed according to Cochrane tool. Weighted mean difference (WMD), calculated with fixed effect model or random effect model, was used to evaluate the effects of GF therapy on left ventricular ejection fraction (LVEF) and Canadian Cardiovascular Society (CCS) angina class. Relative risk (RR) was used to evaluate the effects of GF therapy on all-cause mortality, major adverse cardiovascular events (MACE) and revascularization. Meta-analysis, meta-regression analysis and publication bias analysis were performed by RevMan 5.3 or Stata 15.1 software. Twenty-nine studies involving 2899 IHD patients (1,577 patients in GF group and 1,322 patients in control group) were included. Compared with the control group, GF therapy did not reduce all-cause mortality (RR: 0.82; 95% CI: 0.54–1.24; p = 0.341), MACE [(RR: 0.83; 95% CI: 0.61–1.12; p = 0.227), revascularization (RR: 1.27, 95% CI: 0.82–1.96, p = 0.290) and CCS angina class (WMD: −0.08, 95% CI: −0.36 to 0.20, p = 0.560). However, GF therapy could increase LVEF during short-term follow-up (<1 year). Conclusion: GF for therapeutic angiogenesis was beneficial for increasing LVEF during short-term follow-up (<1 year), however, the therapy was not efficacious in decreasing all-cause mortality, MACE and revascularization.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.024 | 0.040 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.026 | 0.049 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".